README.qmd

---
title: "Accommodating Data Pathologies in Conjoint Studies"
format: gfm
---

## Abstract

Respondent behavior in conjoint studies often deviates from the assumptions of random utility theory. We refer to deviations from normative choice behavior as data pathologies. A variety of models have been developed that attempt to correct for specific pathologies (i.e., screening rules, respondent quality, attribute non-attendance, etc.). While useful, these approaches tend to be both conceptually complex and computational intensive. As such, these approaches have not widely diffused into the practice of marketing research. In this paper we draw on innovations in machine learning to develop a practical approach that relies on (clever) randomization strategies and ensembling to simultaneously accommodate multiple data pathologies in a single model. We provide tips and tricks on how to implement this approach in practice.

## Project Organization

- `/code` Scripts with prefixes (e.g., `01_import-data.R`, `02_clean-data.R`) and functions in `/code/src`.
- `/data` Simulated and real data, the latter not pushed.
- `/figures` PNG images and plots.
- `/output` Output from model runs, not pushed.
- `/presentations` Presentation slides, without knitted PDFs pushed.
- `/private` A catch-all folder for miscellaneous files, not pushed.
- `/renv` Project library, once initialized (see below).
- `/writing` Case studies and the paper, without its knitted PDF pushed.
- `renv.lock` Information on the reproducible environment.

## Reproducible Environment

Every package you install lives in your system library, accessible to all projects. However, packages change. Add a reproducible environment by creating a project library using the `{renv}` package.

- Initialize the project library *once* using `renv::init()`.
- Once you've installed packages, add them to the project library using `renv::snapshot()`.
- If a project library already exists, install the associated packages with `renv::restore()`.

For more details on using GitHub, Quarto, etc. see [Research Assistant Training](https://github.com/marcdotson/ra-training).